Overview

Dataset statistics

Number of variables31
Number of observations17669
Missing cells89843
Missing cells (%)16.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory248.0 B

Variable types

Numeric18
Categorical13

Alerts

numero_de_l_etablissement has a high cardinality: 99 distinct valuesHigh cardinality
etablissement has a high cardinality: 95 distinct valuesHigh cardinality
cle_etab has a high cardinality: 185 distinct valuesHigh cardinality
id_paysage has a high cardinality: 100 distinct valuesHigh cardinality
annee is highly overall correlated with taux_de_chomage_regionalHigh correlation
nombre_de_reponses is highly overall correlated with poids_de_la_discipline and 3 other fieldsHigh correlation
poids_de_la_discipline is highly overall correlated with nombre_de_reponses and 1 other fieldsHigh correlation
taux_dinsertion is highly overall correlated with emplois_stablesHigh correlation
emplois_cadre_ou_professions_intermediaires is highly overall correlated with salaire_net_median_des_emplois_a_temps_plein and 2 other fieldsHigh correlation
emplois_stables is highly overall correlated with taux_dinsertion and 3 other fieldsHigh correlation
emplois_a_temps_plein is highly overall correlated with emplois_stables and 3 other fieldsHigh correlation
salaire_net_median_des_emplois_a_temps_plein is highly overall correlated with emplois_cadre_ou_professions_intermediaires and 4 other fieldsHigh correlation
salaire_brut_annuel_estime is highly overall correlated with emplois_cadre_ou_professions_intermediaires and 4 other fieldsHigh correlation
de_diplomes_boursiers is highly overall correlated with salaire_net_mensuel_regional_1er_quartile and 3 other fieldsHigh correlation
taux_de_chomage_regional is highly overall correlated with annee and 5 other fieldsHigh correlation
salaire_net_mensuel_median_regional is highly overall correlated with emplois_exterieurs_a_la_region_de_luniversite and 3 other fieldsHigh correlation
emplois_cadre is highly overall correlated with emplois_cadre_ou_professions_intermediairesHigh correlation
emplois_exterieurs_a_la_region_de_luniversite is highly overall correlated with salaire_net_mensuel_median_regional and 2 other fieldsHigh correlation
femmes is highly overall correlated with emplois_a_temps_plein and 2 other fieldsHigh correlation
salaire_net_mensuel_regional_1er_quartile is highly overall correlated with de_diplomes_boursiers and 8 other fieldsHigh correlation
salaire_net_mensuel_regional_3eme_quartile is highly overall correlated with salaire_net_mensuel_median_regional and 2 other fieldsHigh correlation
diplome is highly overall correlated with poids_de_la_discipline and 5 other fieldsHigh correlation
numero_de_l_etablissement is highly overall correlated with de_diplomes_boursiers and 7 other fieldsHigh correlation
etablissement is highly overall correlated with de_diplomes_boursiers and 6 other fieldsHigh correlation
code_de_l_academie is highly overall correlated with nombre_de_reponses and 6 other fieldsHigh correlation
academie is highly overall correlated with nombre_de_reponses and 6 other fieldsHigh correlation
code_du_domaine is highly overall correlated with diplome and 4 other fieldsHigh correlation
domaine is highly overall correlated with diplome and 4 other fieldsHigh correlation
code_de_la_discipline is highly overall correlated with diplome and 4 other fieldsHigh correlation
discipline is highly overall correlated with diplome and 4 other fieldsHigh correlation
situation is highly overall correlated with cle_discHigh correlation
cle_disc is highly overall correlated with diplome and 5 other fieldsHigh correlation
id_paysage is highly overall correlated with nombre_de_reponses and 7 other fieldsHigh correlation
diplome is highly imbalanced (65.5%)Imbalance
code_de_l_academie has 325 (1.8%) missing valuesMissing
academie has 325 (1.8%) missing valuesMissing
taux_de_reponse has 321 (1.8%) missing valuesMissing
poids_de_la_discipline has 754 (4.3%) missing valuesMissing
taux_dinsertion has 7884 (44.6%) missing valuesMissing
emplois_cadre_ou_professions_intermediaires has 8838 (50.0%) missing valuesMissing
emplois_stables has 8742 (49.5%) missing valuesMissing
emplois_a_temps_plein has 8810 (49.9%) missing valuesMissing
salaire_net_median_des_emplois_a_temps_plein has 10111 (57.2%) missing valuesMissing
salaire_brut_annuel_estime has 10111 (57.2%) missing valuesMissing
de_diplomes_boursiers has 198 (1.1%) missing valuesMissing
taux_de_chomage_regional has 451 (2.6%) missing valuesMissing
salaire_net_mensuel_median_regional has 590 (3.3%) missing valuesMissing
emplois_cadre has 9313 (52.7%) missing valuesMissing
emplois_exterieurs_a_la_region_de_luniversite has 9143 (51.7%) missing valuesMissing
femmes has 8387 (47.5%) missing valuesMissing
salaire_net_mensuel_regional_1er_quartile has 2585 (14.6%) missing valuesMissing
salaire_net_mensuel_regional_3eme_quartile has 2583 (14.6%) missing valuesMissing
id_paysage has 325 (1.8%) missing valuesMissing
nombre_de_reponses has 283 (1.6%) zerosZeros
poids_de_la_discipline has 263 (1.5%) zerosZeros

Reproduction

Analysis started2023-04-11 03:26:22.962067
Analysis finished2023-04-11 03:27:13.329860
Duration50.37 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

annee
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.9106
Minimum2010
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:13.419650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2015
Q32017
95-th percentile2019
Maximum2019
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6775498
Coefficient of variation (CV)0.0013288678
Kurtosis-1.0980887
Mean2014.9106
Median Absolute Deviation (MAD)2
Skewness-0.088709335
Sum35601455
Variance7.1692729
MonotonicityNot monotonic
2023-04-11T05:27:13.540205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2012 2000
11.3%
2013 1986
11.2%
2016 1970
11.1%
2014 1962
11.1%
2019 1948
11.0%
2015 1930
10.9%
2017 1924
10.9%
2018 1924
10.9%
2011 1097
6.2%
2010 928
5.3%
ValueCountFrequency (%)
2010 928
5.3%
2011 1097
6.2%
2012 2000
11.3%
2013 1986
11.2%
2014 1962
11.1%
2015 1930
10.9%
2016 1970
11.1%
2017 1924
10.9%
2018 1924
10.9%
2019 1948
11.0%
ValueCountFrequency (%)
2019 1948
11.0%
2018 1924
10.9%
2017 1924
10.9%
2016 1970
11.1%
2015 1930
10.9%
2014 1962
11.1%
2013 1986
11.2%
2012 2000
11.3%
2011 1097
6.2%
2010 928
5.3%

diplome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
MASTER LMD
16528 
MASTER ENS
 
1141

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters176690
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMASTER LMD
2nd rowMASTER LMD
3rd rowMASTER LMD
4th rowMASTER LMD
5th rowMASTER LMD

Common Values

ValueCountFrequency (%)
MASTER LMD 16528
93.5%
MASTER ENS 1141
 
6.5%

Length

2023-04-11T05:27:13.651320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T05:27:13.786723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
master 17669
50.0%
lmd 16528
46.8%
ens 1141
 
3.2%

Most occurring characters

ValueCountFrequency (%)
M 34197
19.4%
S 18810
10.6%
E 18810
10.6%
A 17669
10.0%
T 17669
10.0%
R 17669
10.0%
17669
10.0%
L 16528
9.4%
D 16528
9.4%
N 1141
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 159021
90.0%
Space Separator 17669
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 34197
21.5%
S 18810
11.8%
E 18810
11.8%
A 17669
11.1%
T 17669
11.1%
R 17669
11.1%
L 16528
10.4%
D 16528
10.4%
N 1141
 
0.7%
Space Separator
ValueCountFrequency (%)
17669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 159021
90.0%
Common 17669
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 34197
21.5%
S 18810
11.8%
E 18810
11.8%
A 17669
11.1%
T 17669
11.1%
R 17669
11.1%
L 16528
10.4%
D 16528
10.4%
N 1141
 
0.7%
Common
ValueCountFrequency (%)
17669
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 34197
19.4%
S 18810
10.6%
E 18810
10.6%
A 17669
10.0%
T 17669
10.0%
R 17669
10.0%
17669
10.0%
L 16528
9.4%
D 16528
9.4%
N 1141
 
0.6%

numero_de_l_etablissement
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct99
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
UNIV
 
325
0211237F
 
325
0673021V
 
325
0134009M
 
323
0931238R
 
323
Other values (94)
16048 

Length

Max length8
Median length8
Mean length7.9264248
Min length4

Characters and Unicode

Total characters140052
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0060931E
2nd row0060931E
3rd row0134009M
4th row0134009M
5th row0141408E

Common Values

ValueCountFrequency (%)
UNIV 325
 
1.8%
0211237F 325
 
1.8%
0673021V 325
 
1.8%
0134009M 323
 
1.8%
0931238R 323
 
1.8%
0251215K 321
 
1.8%
0542493S 319
 
1.8%
0860856N 319
 
1.8%
0801344B 315
 
1.8%
0511296G 311
 
1.8%
Other values (89) 14463
81.9%

Length

2023-04-11T05:27:13.906152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
univ 325
 
1.8%
0211237f 325
 
1.8%
0673021v 325
 
1.8%
0134009m 323
 
1.8%
0931238r 323
 
1.8%
0251215k 321
 
1.8%
0542493s 319
 
1.8%
0860856n 319
 
1.8%
0801344b 315
 
1.8%
0511296g 311
 
1.8%
Other values (89) 14463
81.9%

Most occurring characters

ValueCountFrequency (%)
0 27451
19.6%
1 15019
10.7%
3 11899
8.5%
7 11128
7.9%
9 10993
7.8%
5 9578
 
6.8%
6 9452
 
6.7%
4 9320
 
6.7%
2 8757
 
6.3%
8 7811
 
5.6%
Other values (24) 18644
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121408
86.7%
Uppercase Letter 18644
 
13.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1996
 
10.7%
E 1444
 
7.7%
J 1374
 
7.4%
U 1132
 
6.1%
F 1125
 
6.0%
R 1004
 
5.4%
K 971
 
5.2%
V 960
 
5.1%
M 914
 
4.9%
G 904
 
4.8%
Other values (14) 6820
36.6%
Decimal Number
ValueCountFrequency (%)
0 27451
22.6%
1 15019
12.4%
3 11899
9.8%
7 11128
9.2%
9 10993
9.1%
5 9578
 
7.9%
6 9452
 
7.8%
4 9320
 
7.7%
2 8757
 
7.2%
8 7811
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 121408
86.7%
Latin 18644
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1996
 
10.7%
E 1444
 
7.7%
J 1374
 
7.4%
U 1132
 
6.1%
F 1125
 
6.0%
R 1004
 
5.4%
K 971
 
5.2%
V 960
 
5.1%
M 914
 
4.9%
G 904
 
4.8%
Other values (14) 6820
36.6%
Common
ValueCountFrequency (%)
0 27451
22.6%
1 15019
12.4%
3 11899
9.8%
7 11128
9.2%
9 10993
9.1%
5 9578
 
7.9%
6 9452
 
7.8%
4 9320
 
7.7%
2 8757
 
7.2%
8 7811
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27451
19.6%
1 15019
10.7%
3 11899
8.5%
7 11128
7.9%
9 10993
7.8%
5 9578
 
6.8%
6 9452
 
6.7%
4 9320
 
6.7%
2 8757
 
6.3%
8 7811
 
5.6%
Other values (24) 18644
13.3%

etablissement
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct95
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
Strasbourg
 
325
Toutes universités et établissements assimilés
 
325
Dijon - Bourgogne
 
325
Université Sorbonne Paris Nord
 
323
Aix-Marseille
 
323
Other values (90)
16048 

Length

Max length48
Median length31
Mean length17.763031
Min length5

Characters and Unicode

Total characters313855
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNice - Sophia-Antipolis
2nd rowNice - Sophia-Antipolis
3rd rowAix-Marseille
4th rowAix-Marseille
5th rowCaen Normandie

Common Values

ValueCountFrequency (%)
Strasbourg 325
 
1.8%
Toutes universités et établissements assimilés 325
 
1.8%
Dijon - Bourgogne 325
 
1.8%
Université Sorbonne Paris Nord 323
 
1.8%
Aix-Marseille 323
 
1.8%
Nantes 323
 
1.8%
Besançon - Franche-Comté 321
 
1.8%
Poitiers 319
 
1.8%
Lorraine 319
 
1.8%
Amiens - Picardie Jules-Verne 315
 
1.8%
Other values (85) 14451
81.8%

Length

2023-04-11T05:27:14.056163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6405
 
13.5%
paris 2215
 
4.7%
1 1125
 
2.4%
université 868
 
1.8%
2 849
 
1.8%
sorbonne 835
 
1.8%
3 786
 
1.7%
jean 739
 
1.6%
lyon 691
 
1.5%
de 612
 
1.3%
Other values (149) 32229
68.1%

Most occurring characters

ValueCountFrequency (%)
e 33949
 
10.8%
29685
 
9.5%
n 24929
 
7.9%
a 21123
 
6.7%
i 20625
 
6.6%
s 19248
 
6.1%
r 19185
 
6.1%
o 17441
 
5.6%
l 13790
 
4.4%
t 13315
 
4.2%
Other values (51) 100565
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 227991
72.6%
Uppercase Letter 39997
 
12.7%
Space Separator 29685
 
9.5%
Dash Punctuation 11925
 
3.8%
Decimal Number 3493
 
1.1%
Other Punctuation 764
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33949
14.9%
n 24929
10.9%
a 21123
9.3%
i 20625
9.0%
s 19248
8.4%
r 19185
8.4%
o 17441
7.6%
l 13790
 
6.0%
t 13315
 
5.8%
u 9081
 
4.0%
Other values (19) 35305
15.5%
Uppercase Letter
ValueCountFrequency (%)
P 6254
15.6%
A 3434
 
8.6%
S 3413
 
8.5%
L 3237
 
8.1%
C 3120
 
7.8%
M 2617
 
6.5%
B 2541
 
6.4%
N 1864
 
4.7%
R 1740
 
4.4%
V 1702
 
4.3%
Other values (13) 10075
25.2%
Decimal Number
ValueCountFrequency (%)
1 1415
40.5%
2 849
24.3%
3 786
22.5%
8 259
 
7.4%
7 132
 
3.8%
4 52
 
1.5%
Space Separator
ValueCountFrequency (%)
29685
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11925
100.0%
Other Punctuation
ValueCountFrequency (%)
' 764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 267988
85.4%
Common 45867
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33949
12.7%
n 24929
 
9.3%
a 21123
 
7.9%
i 20625
 
7.7%
s 19248
 
7.2%
r 19185
 
7.2%
o 17441
 
6.5%
l 13790
 
5.1%
t 13315
 
5.0%
u 9081
 
3.4%
Other values (42) 75302
28.1%
Common
ValueCountFrequency (%)
29685
64.7%
- 11925
26.0%
1 1415
 
3.1%
2 849
 
1.9%
3 786
 
1.7%
' 764
 
1.7%
8 259
 
0.6%
7 132
 
0.3%
4 52
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308297
98.2%
None 5558
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33949
 
11.0%
29685
 
9.6%
n 24929
 
8.1%
a 21123
 
6.9%
i 20625
 
6.7%
s 19248
 
6.2%
r 19185
 
6.2%
o 17441
 
5.7%
l 13790
 
4.5%
t 13315
 
4.3%
Other values (45) 95007
30.8%
None
ValueCountFrequency (%)
é 4121
74.1%
ç 491
 
8.8%
è 310
 
5.6%
ô 277
 
5.0%
É 219
 
3.9%
î 140
 
2.5%

code_de_l_academie
Categorical

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)0.2%
Missing325
Missing (%)1.8%
Memory size138.2 KiB
A25
1318 
A09
1178 
A01
 
1155
A24
 
1116
A14
 
1025
Other values (24)
11552 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters52032
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA23
2nd rowA23
3rd rowA02
4th rowA02
5th rowA70

Common Values

ValueCountFrequency (%)
A25 1318
 
7.5%
A09 1178
 
6.7%
A01 1155
 
6.5%
A24 1116
 
6.3%
A14 1025
 
5.8%
A10 991
 
5.6%
A17 861
 
4.9%
A11 830
 
4.7%
A70 826
 
4.7%
A16 713
 
4.0%
Other values (19) 7331
41.5%

Length

2023-04-11T05:27:14.175712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a25 1318
 
7.6%
a09 1178
 
6.8%
a01 1155
 
6.7%
a24 1116
 
6.4%
a14 1025
 
5.9%
a10 991
 
5.7%
a17 861
 
5.0%
a11 830
 
4.8%
a70 826
 
4.8%
a16 713
 
4.1%
Other values (19) 7331
42.3%

Most occurring characters

ValueCountFrequency (%)
A 17344
33.3%
1 8906
17.1%
0 7369
14.2%
2 5435
 
10.4%
4 2867
 
5.5%
7 2260
 
4.3%
5 1897
 
3.6%
3 1786
 
3.4%
9 1581
 
3.0%
8 1506
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34688
66.7%
Uppercase Letter 17344
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8906
25.7%
0 7369
21.2%
2 5435
15.7%
4 2867
 
8.3%
7 2260
 
6.5%
5 1897
 
5.5%
3 1786
 
5.1%
9 1581
 
4.6%
8 1506
 
4.3%
6 1081
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 17344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34688
66.7%
Latin 17344
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8906
25.7%
0 7369
21.2%
2 5435
15.7%
4 2867
 
8.3%
7 2260
 
6.5%
5 1897
 
5.5%
3 1786
 
5.1%
9 1581
 
4.6%
8 1506
 
4.3%
6 1081
 
3.1%
Latin
ValueCountFrequency (%)
A 17344
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17344
33.3%
1 8906
17.1%
0 7369
14.2%
2 5435
 
10.4%
4 2867
 
5.5%
7 2260
 
4.3%
5 1897
 
3.6%
3 1786
 
3.4%
9 1581
 
3.0%
8 1506
 
2.9%

academie
Categorical

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)0.2%
Missing325
Missing (%)1.8%
Memory size138.2 KiB
Versailles
1318 
Lille
1178 
Paris
 
1155
Créteil
 
1116
Rennes
 
1025
Other values (24)
11552 

Length

Max length19
Median length13
Mean length7.7739276
Min length4

Characters and Unicode

Total characters134831
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNice
2nd rowNice
3rd rowAix-Marseille
4th rowAix-Marseille
5th rowNormandie

Common Values

ValueCountFrequency (%)
Versailles 1318
 
7.5%
Lille 1178
 
6.7%
Paris 1155
 
6.5%
Créteil 1116
 
6.3%
Rennes 1025
 
5.8%
Lyon 991
 
5.6%
Nantes 861
 
4.9%
Montpellier 830
 
4.7%
Normandie 826
 
4.7%
Toulouse 713
 
4.0%
Other values (19) 7331
41.5%

Length

2023-04-11T05:27:14.297531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
versailles 1318
 
7.5%
lille 1178
 
6.7%
paris 1155
 
6.5%
créteil 1116
 
6.3%
rennes 1025
 
5.8%
lyon 991
 
5.6%
nantes 861
 
4.9%
montpellier 830
 
4.7%
normandie 826
 
4.7%
toulouse 713
 
4.0%
Other values (21) 7663
43.4%

Most occurring characters

ValueCountFrequency (%)
e 18445
13.7%
l 11447
 
8.5%
r 11389
 
8.4%
s 10954
 
8.1%
i 10829
 
8.0%
n 9931
 
7.4%
o 9254
 
6.9%
a 8263
 
6.1%
t 4625
 
3.4%
u 4065
 
3.0%
Other values (29) 35629
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 113201
84.0%
Uppercase Letter 19487
 
14.5%
Dash Punctuation 1811
 
1.3%
Space Separator 332
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 18445
16.3%
l 11447
10.1%
r 11389
10.1%
s 10954
9.7%
i 10829
9.6%
n 9931
8.8%
o 9254
8.2%
a 8263
7.3%
t 4625
 
4.1%
u 4065
 
3.6%
Other values (12) 13999
12.4%
Uppercase Letter
ValueCountFrequency (%)
L 2713
13.9%
N 2537
13.0%
P 1765
9.1%
C 1732
8.9%
R 1702
8.7%
M 1683
8.6%
V 1318
6.8%
T 1303
6.7%
B 1035
 
5.3%
G 930
 
4.8%
Other values (5) 2769
14.2%
Dash Punctuation
ValueCountFrequency (%)
- 1811
100.0%
Space Separator
ValueCountFrequency (%)
332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132688
98.4%
Common 2143
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 18445
13.9%
l 11447
 
8.6%
r 11389
 
8.6%
s 10954
 
8.3%
i 10829
 
8.2%
n 9931
 
7.5%
o 9254
 
7.0%
a 8263
 
6.2%
t 4625
 
3.5%
u 4065
 
3.1%
Other values (27) 33486
25.2%
Common
ValueCountFrequency (%)
- 1811
84.5%
332
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132368
98.2%
None 2463
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 18445
13.9%
l 11447
 
8.6%
r 11389
 
8.6%
s 10954
 
8.3%
i 10829
 
8.2%
n 9931
 
7.5%
o 9254
 
7.0%
a 8263
 
6.2%
t 4625
 
3.5%
u 4065
 
3.1%
Other values (27) 33166
25.1%
None
ValueCountFrequency (%)
é 2038
82.7%
ç 425
 
17.3%

code_du_domaine
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
STS
5874 
DEG
5016 
SHS
4582 
MEEF
1141 
LLA
1056 

Length

Max length4
Median length3
Mean length3.0645764
Min length3

Characters and Unicode

Total characters54148
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEG
2nd rowSHS
3rd rowSHS
4th rowSHS
5th rowDEG

Common Values

ValueCountFrequency (%)
STS 5874
33.2%
DEG 5016
28.4%
SHS 4582
25.9%
MEEF 1141
 
6.5%
LLA 1056
 
6.0%

Length

2023-04-11T05:27:14.431120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T05:27:14.596632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
sts 5874
33.2%
deg 5016
28.4%
shs 4582
25.9%
meef 1141
 
6.5%
lla 1056
 
6.0%

Most occurring characters

ValueCountFrequency (%)
S 20912
38.6%
E 7298
 
13.5%
T 5874
 
10.8%
D 5016
 
9.3%
G 5016
 
9.3%
H 4582
 
8.5%
L 2112
 
3.9%
M 1141
 
2.1%
F 1141
 
2.1%
A 1056
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54148
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 20912
38.6%
E 7298
 
13.5%
T 5874
 
10.8%
D 5016
 
9.3%
G 5016
 
9.3%
H 4582
 
8.5%
L 2112
 
3.9%
M 1141
 
2.1%
F 1141
 
2.1%
A 1056
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 20912
38.6%
E 7298
 
13.5%
T 5874
 
10.8%
D 5016
 
9.3%
G 5016
 
9.3%
H 4582
 
8.5%
L 2112
 
3.9%
M 1141
 
2.1%
F 1141
 
2.1%
A 1056
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 20912
38.6%
E 7298
 
13.5%
T 5874
 
10.8%
D 5016
 
9.3%
G 5016
 
9.3%
H 4582
 
8.5%
L 2112
 
3.9%
M 1141
 
2.1%
F 1141
 
2.1%
A 1056
 
2.0%

domaine
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
Sciences, technologies et santé
5874 
Droit, économie et gestion
5016 
Sciences humaines et sociales
4582 
Masters enseignement
1141 
Lettres, langues, arts
1056 

Length

Max length31
Median length29
Mean length27.813685
Min length20

Characters and Unicode

Total characters491440
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDroit, économie et gestion
2nd rowSciences humaines et sociales
3rd rowSciences humaines et sociales
4th rowSciences humaines et sociales
5th rowDroit, économie et gestion

Common Values

ValueCountFrequency (%)
Sciences, technologies et santé 5874
33.2%
Droit, économie et gestion 5016
28.4%
Sciences humaines et sociales 4582
25.9%
Masters enseignement 1141
 
6.5%
Lettres, langues, arts 1056
 
6.0%

Length

2023-04-11T05:27:14.729582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T05:27:14.892482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
et 15472
23.0%
sciences 10456
15.5%
technologies 5874
 
8.7%
santé 5874
 
8.7%
droit 5016
 
7.4%
économie 5016
 
7.4%
gestion 5016
 
7.4%
humaines 4582
 
6.8%
sociales 4582
 
6.8%
masters 1141
 
1.7%
Other values (4) 4309
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e 76201
15.5%
49669
10.1%
s 47557
9.7%
t 42702
8.7%
i 41683
8.5%
n 41297
8.4%
o 36394
7.4%
c 36384
7.4%
a 18291
 
3.7%
g 13087
 
2.7%
Other values (11) 88175
17.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 411100
83.7%
Space Separator 49669
 
10.1%
Uppercase Letter 17669
 
3.6%
Other Punctuation 13002
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 76201
18.5%
s 47557
11.6%
t 42702
10.4%
i 41683
10.1%
n 41297
10.0%
o 36394
8.9%
c 36384
8.9%
a 18291
 
4.4%
g 13087
 
3.2%
l 11512
 
2.8%
Other values (5) 45992
11.2%
Uppercase Letter
ValueCountFrequency (%)
S 10456
59.2%
D 5016
28.4%
M 1141
 
6.5%
L 1056
 
6.0%
Space Separator
ValueCountFrequency (%)
49669
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428769
87.2%
Common 62671
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 76201
17.8%
s 47557
11.1%
t 42702
10.0%
i 41683
9.7%
n 41297
9.6%
o 36394
8.5%
c 36384
8.5%
a 18291
 
4.3%
g 13087
 
3.1%
l 11512
 
2.7%
Other values (9) 63661
14.8%
Common
ValueCountFrequency (%)
49669
79.3%
, 13002
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 480550
97.8%
None 10890
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 76201
15.9%
49669
10.3%
s 47557
9.9%
t 42702
8.9%
i 41683
8.7%
n 41297
8.6%
o 36394
7.6%
c 36384
7.6%
a 18291
 
3.8%
g 13087
 
2.7%
Other values (10) 77285
16.1%
None
ValueCountFrequency (%)
é 10890
100.0%
Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
disc07
1228 
disc12
1201 
disc01
1200 
disc04
 
1079
disc11
 
1077
Other values (15)
11884 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters106014
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdisc04
2nd rowdisc08
3rd rowdisc08
4th rowdisc10
5th rowdisc01

Common Values

ValueCountFrequency (%)
disc07 1228
 
7.0%
disc12 1201
 
6.8%
disc01 1200
 
6.8%
disc04 1079
 
6.1%
disc11 1077
 
6.1%
disc06 1056
 
6.0%
disc18 1037
 
5.9%
disc16 1035
 
5.9%
disc02 1033
 
5.8%
disc14 977
 
5.5%
Other values (10) 6746
38.2%

Length

2023-04-11T05:27:15.021022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disc07 1228
 
7.0%
disc12 1201
 
6.8%
disc01 1200
 
6.8%
disc04 1079
 
6.1%
disc11 1077
 
6.1%
disc06 1056
 
6.0%
disc18 1037
 
5.9%
disc16 1035
 
5.9%
disc02 1033
 
5.8%
disc14 977
 
5.5%
Other values (10) 6746
38.2%

Most occurring characters

ValueCountFrequency (%)
d 17669
16.7%
i 17669
16.7%
s 17669
16.7%
c 17669
16.7%
1 11100
10.5%
0 9639
9.1%
2 2296
 
2.2%
6 2091
 
2.0%
7 2089
 
2.0%
4 2056
 
1.9%
Other values (4) 6067
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70676
66.7%
Decimal Number 35338
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11100
31.4%
0 9639
27.3%
2 2296
 
6.5%
6 2091
 
5.9%
7 2089
 
5.9%
4 2056
 
5.8%
8 1959
 
5.5%
3 1924
 
5.4%
5 1580
 
4.5%
9 604
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
d 17669
25.0%
i 17669
25.0%
s 17669
25.0%
c 17669
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70676
66.7%
Common 35338
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11100
31.4%
0 9639
27.3%
2 2296
 
6.5%
6 2091
 
5.9%
7 2089
 
5.9%
4 2056
 
5.8%
8 1959
 
5.5%
3 1924
 
5.4%
5 1580
 
4.5%
9 604
 
1.7%
Latin
ValueCountFrequency (%)
d 17669
25.0%
i 17669
25.0%
s 17669
25.0%
c 17669
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 17669
16.7%
i 17669
16.7%
s 17669
16.7%
c 17669
16.7%
1 11100
10.5%
0 9639
9.1%
2 2296
 
2.2%
6 2091
 
2.0%
7 2089
 
2.0%
4 2056
 
1.9%
Other values (4) 6067
 
5.7%

discipline
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
Ensemble sciences humaines et sociales
1228 
Ensemble sciences, technologies et santé
1201 
Ensemble formations juridiques, économiques et de gestion
1200 
Gestion
 
1079
Autres sciences humaines et sociales
 
1077
Other values (15)
11884 

Length

Max length57
Median length33
Mean length26.72149
Min length5

Characters and Unicode

Total characters472142
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGestion
2nd rowHistoire-géographie
3rd rowHistoire-géographie
4th rowInformation communication
5th rowEnsemble formations juridiques, économiques et de gestion

Common Values

ValueCountFrequency (%)
Ensemble sciences humaines et sociales 1228
 
7.0%
Ensemble sciences, technologies et santé 1201
 
6.8%
Ensemble formations juridiques, économiques et de gestion 1200
 
6.8%
Gestion 1079
 
6.1%
Autres sciences humaines et sociales 1077
 
6.1%
Lettres, langues, arts 1056
 
6.0%
Masters enseignement 1037
 
5.9%
Informatique 1035
 
5.9%
Droit 1033
 
5.8%
Sciences fondamentales 977
 
5.5%
Other values (10) 6746
38.2%

Length

2023-04-11T05:27:15.141491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
et 7261
 
12.0%
sciences 7144
 
11.8%
de 4694
 
7.8%
ensemble 3629
 
6.0%
gestion 3016
 
5.0%
autres 2675
 
4.4%
humaines 2305
 
3.8%
sociales 2305
 
3.8%
technologies 2062
 
3.4%
santé 2062
 
3.4%
Other values (25) 23388
38.6%

Most occurring characters

ValueCountFrequency (%)
e 70999
15.0%
s 46795
 
9.9%
42872
 
9.1%
n 37447
 
7.9%
i 37148
 
7.9%
t 30973
 
6.6%
o 28374
 
6.0%
c 23769
 
5.0%
a 19273
 
4.1%
r 17452
 
3.7%
Other values (31) 117040
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 403187
85.4%
Space Separator 42872
 
9.1%
Uppercase Letter 17855
 
3.8%
Other Punctuation 7306
 
1.5%
Dash Punctuation 922
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70999
17.6%
s 46795
11.6%
n 37447
9.3%
i 37148
9.2%
t 30973
7.7%
o 28374
 
7.0%
c 23769
 
5.9%
a 19273
 
4.8%
r 17452
 
4.3%
m 16349
 
4.1%
Other values (13) 74608
18.5%
Uppercase Letter
ValueCountFrequency (%)
E 3691
20.7%
S 2777
15.6%
A 2675
15.0%
I 1828
10.2%
M 1141
 
6.4%
G 1079
 
6.0%
L 1056
 
5.9%
D 1033
 
5.8%
É 967
 
5.4%
H 922
 
5.2%
Other values (2) 686
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 6173
84.5%
' 843
 
11.5%
. 186
 
2.5%
: 104
 
1.4%
Space Separator
ValueCountFrequency (%)
42872
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 421042
89.2%
Common 51100
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70999
16.9%
s 46795
11.1%
n 37447
8.9%
i 37148
8.8%
t 30973
 
7.4%
o 28374
 
6.7%
c 23769
 
5.6%
a 19273
 
4.6%
r 17452
 
4.1%
m 16349
 
3.9%
Other values (25) 92463
22.0%
Common
ValueCountFrequency (%)
42872
83.9%
, 6173
 
12.1%
- 922
 
1.8%
' 843
 
1.6%
. 186
 
0.4%
: 104
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465307
98.6%
None 6835
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 70999
15.3%
s 46795
10.1%
42872
 
9.2%
n 37447
 
8.0%
i 37148
 
8.0%
t 30973
 
6.7%
o 28374
 
6.1%
c 23769
 
5.1%
a 19273
 
4.1%
r 17452
 
3.8%
Other values (29) 110205
23.7%
None
ValueCountFrequency (%)
é 5868
85.9%
É 967
 
14.1%

situation
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
30 mois après le diplôme
9847 
18 mois après le diplôme
7822 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters424056
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30 mois après le diplôme
2nd row30 mois après le diplôme
3rd row30 mois après le diplôme
4th row30 mois après le diplôme
5th row30 mois après le diplôme

Common Values

ValueCountFrequency (%)
30 mois après le diplôme 9847
55.7%
18 mois après le diplôme 7822
44.3%

Length

2023-04-11T05:27:15.256433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T05:27:15.382899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mois 17669
20.0%
après 17669
20.0%
le 17669
20.0%
diplôme 17669
20.0%
30 9847
11.1%
18 7822
8.9%

Most occurring characters

ValueCountFrequency (%)
70676
16.7%
p 35338
 
8.3%
m 35338
 
8.3%
i 35338
 
8.3%
s 35338
 
8.3%
l 35338
 
8.3%
e 35338
 
8.3%
d 17669
 
4.2%
o 17669
 
4.2%
a 17669
 
4.2%
Other values (7) 88345
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 318042
75.0%
Space Separator 70676
 
16.7%
Decimal Number 35338
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 35338
11.1%
m 35338
11.1%
i 35338
11.1%
s 35338
11.1%
l 35338
11.1%
e 35338
11.1%
d 17669
5.6%
o 17669
5.6%
a 17669
5.6%
ô 17669
5.6%
Other values (2) 35338
11.1%
Decimal Number
ValueCountFrequency (%)
3 9847
27.9%
0 9847
27.9%
1 7822
22.1%
8 7822
22.1%
Space Separator
ValueCountFrequency (%)
70676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 318042
75.0%
Common 106014
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 35338
11.1%
m 35338
11.1%
i 35338
11.1%
s 35338
11.1%
l 35338
11.1%
e 35338
11.1%
d 17669
5.6%
o 17669
5.6%
a 17669
5.6%
ô 17669
5.6%
Other values (2) 35338
11.1%
Common
ValueCountFrequency (%)
70676
66.7%
3 9847
 
9.3%
0 9847
 
9.3%
1 7822
 
7.4%
8 7822
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 388718
91.7%
None 35338
 
8.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70676
18.2%
p 35338
9.1%
m 35338
9.1%
i 35338
9.1%
s 35338
9.1%
l 35338
9.1%
e 35338
9.1%
d 17669
 
4.5%
o 17669
 
4.5%
a 17669
 
4.5%
Other values (5) 53007
13.6%
None
ValueCountFrequency (%)
ô 17669
50.0%
è 17669
50.0%

nombre_de_reponses
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct659
Distinct (%)3.7%
Missing47
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean127.54489
Minimum0
Maximum13587
Zeros283
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:15.522060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q114
median35
Q383
95-th percentile295
Maximum13587
Range13587
Interquartile range (IQR)69

Descriptive statistics

Standard deviation626.88186
Coefficient of variation (CV)4.9149901
Kurtosis218.22422
Mean127.54489
Median Absolute Deviation (MAD)26
Skewness13.568796
Sum2247596
Variance392980.87
MonotonicityNot monotonic
2023-04-11T05:27:15.679838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 345
 
2.0%
5 329
 
1.9%
4 327
 
1.9%
1 312
 
1.8%
9 308
 
1.7%
6 305
 
1.7%
10 301
 
1.7%
7 299
 
1.7%
17 291
 
1.6%
15 290
 
1.6%
Other values (649) 14515
82.1%
ValueCountFrequency (%)
0 283
1.6%
1 312
1.8%
2 284
1.6%
3 290
1.6%
4 327
1.9%
5 329
1.9%
6 305
1.7%
7 299
1.7%
8 275
1.6%
9 308
1.7%
ValueCountFrequency (%)
13587 2
< 0.1%
13165 2
< 0.1%
13159 2
< 0.1%
12839 2
< 0.1%
12584 2
< 0.1%
12503 2
< 0.1%
11849 2
< 0.1%
11488 1
< 0.1%
11433 2
< 0.1%
11122 1
< 0.1%

taux_de_reponse
Real number (ℝ)

Distinct90
Distinct (%)0.5%
Missing321
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean71.903505
Minimum0
Maximum100
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:15.841899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q165
median73
Q381
95-th percentile92
Maximum100
Range100
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.815568
Coefficient of variation (CV)0.1921404
Kurtosis1.1270693
Mean71.903505
Median Absolute Deviation (MAD)8
Skewness-0.6530884
Sum1247382
Variance190.86992
MonotonicityNot monotonic
2023-04-11T05:27:15.994462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 664
 
3.8%
67 637
 
3.6%
73 610
 
3.5%
74 591
 
3.3%
71 585
 
3.3%
79 579
 
3.3%
78 573
 
3.2%
80 569
 
3.2%
76 564
 
3.2%
77 537
 
3.0%
Other values (80) 11439
64.7%
ValueCountFrequency (%)
0 7
< 0.1%
2 3
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
14 7
< 0.1%
15 2
 
< 0.1%
18 2
 
< 0.1%
19 3
< 0.1%
ValueCountFrequency (%)
100 522
3.0%
98 9
 
0.1%
97 27
 
0.2%
96 43
 
0.2%
95 64
 
0.4%
94 71
 
0.4%
93 102
 
0.6%
92 150
 
0.8%
91 114
 
0.6%
90 197
 
1.1%

poids_de_la_discipline
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct96
Distinct (%)0.6%
Missing754
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean18.434821
Minimum0
Maximum100
Zeros263
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:16.154285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q322
95-th percentile87
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.460317
Coefficient of variation (CV)1.2726089
Kurtosis4.7874103
Mean18.434821
Median Absolute Deviation (MAD)6
Skewness2.2663585
Sum311825
Variance550.38648
MonotonicityNot monotonic
2023-04-11T05:27:16.302531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1177
 
6.7%
5 1107
 
6.3%
2 1047
 
5.9%
3 984
 
5.6%
6 969
 
5.5%
1 879
 
5.0%
7 865
 
4.9%
100 781
 
4.4%
8 672
 
3.8%
9 618
 
3.5%
Other values (86) 7816
44.2%
(Missing) 754
 
4.3%
ValueCountFrequency (%)
0 263
 
1.5%
1 879
5.0%
2 1047
5.9%
3 984
5.6%
4 1177
6.7%
5 1107
6.3%
6 969
5.5%
7 865
4.9%
8 672
3.8%
9 618
3.5%
ValueCountFrequency (%)
100 781
4.4%
97 6
 
< 0.1%
96 4
 
< 0.1%
93 4
 
< 0.1%
92 3
 
< 0.1%
91 10
 
0.1%
90 8
 
< 0.1%
89 13
 
0.1%
88 13
 
0.1%
87 8
 
< 0.1%

taux_dinsertion
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)0.5%
Missing7884
Missing (%)44.6%
Infinite0
Infinite (%)0.0%
Mean89.58835
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:16.453390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile77
Q186
median91
Q394
95-th percentile99
Maximum100
Range94
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.748337
Coefficient of variation (CV)0.075326056
Kurtosis5.3814142
Mean89.58835
Median Absolute Deviation (MAD)4
Skewness-1.2206496
Sum876622
Variance45.540052
MonotonicityNot monotonic
2023-04-11T05:27:16.602605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
94 667
 
3.8%
93 650
 
3.7%
91 646
 
3.7%
90 631
 
3.6%
92 623
 
3.5%
95 541
 
3.1%
89 536
 
3.0%
97 508
 
2.9%
88 480
 
2.7%
87 444
 
2.5%
Other values (36) 4059
23.0%
(Missing) 7884
44.6%
ValueCountFrequency (%)
6 1
 
< 0.1%
13 1
 
< 0.1%
34 1
 
< 0.1%
37 1
 
< 0.1%
56 1
 
< 0.1%
60 1
 
< 0.1%
61 2
 
< 0.1%
62 2
 
< 0.1%
63 2
 
< 0.1%
64 6
< 0.1%
ValueCountFrequency (%)
100 365
2.1%
99 159
 
0.9%
98 358
2.0%
97 508
2.9%
96 438
2.5%
95 541
3.1%
94 667
3.8%
93 650
3.7%
92 623
3.5%
91 646
3.7%

emplois_cadre_ou_professions_intermediaires
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct69
Distinct (%)0.8%
Missing8838
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean87.046767
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:16.755572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile66
Q181
median90
Q395
95-th percentile100
Maximum100
Range97
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.757071
Coefficient of variation (CV)0.12357806
Kurtosis2.1432408
Mean87.046767
Median Absolute Deviation (MAD)6
Skewness-1.2487421
Sum768710
Variance115.71457
MonotonicityNot monotonic
2023-04-11T05:27:16.906990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 539
 
3.1%
97 514
 
2.9%
95 478
 
2.7%
94 471
 
2.7%
98 436
 
2.5%
93 414
 
2.3%
92 397
 
2.2%
96 388
 
2.2%
91 387
 
2.2%
90 327
 
1.9%
Other values (59) 4480
25.4%
(Missing) 8838
50.0%
ValueCountFrequency (%)
3 1
 
< 0.1%
11 1
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
26 1
 
< 0.1%
32 1
 
< 0.1%
36 2
< 0.1%
37 3
< 0.1%
39 1
 
< 0.1%
ValueCountFrequency (%)
100 539
3.1%
99 192
 
1.1%
98 436
2.5%
97 514
2.9%
96 388
2.2%
95 478
2.7%
94 471
2.7%
93 414
2.3%
92 397
2.2%
91 387
2.2%

emplois_stables
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)0.9%
Missing8742
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean70.216086
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:17.059529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q159
median72
Q383
95-th percentile94
Maximum100
Range88
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.223996
Coefficient of variation (CV)0.2310581
Kurtosis-0.43705894
Mean70.216086
Median Absolute Deviation (MAD)12
Skewness-0.41891636
Sum626819
Variance263.21803
MonotonicityNot monotonic
2023-04-11T05:27:17.201532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 240
 
1.4%
82 232
 
1.3%
87 224
 
1.3%
76 211
 
1.2%
84 205
 
1.2%
83 202
 
1.1%
70 201
 
1.1%
79 200
 
1.1%
77 200
 
1.1%
78 200
 
1.1%
Other values (73) 6812
38.6%
(Missing) 8742
49.5%
ValueCountFrequency (%)
12 1
 
< 0.1%
15 1
 
< 0.1%
19 3
 
< 0.1%
21 2
 
< 0.1%
22 1
 
< 0.1%
23 6
< 0.1%
24 5
< 0.1%
25 5
< 0.1%
26 10
0.1%
27 10
0.1%
ValueCountFrequency (%)
100 68
0.4%
99 14
 
0.1%
98 66
0.4%
97 105
0.6%
96 75
0.4%
95 77
0.4%
94 103
0.6%
93 111
0.6%
92 112
0.6%
91 115
0.7%

emplois_a_temps_plein
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)0.7%
Missing8810
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean92.417203
Minimum37
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:17.367408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile73
Q190
median96
Q398
95-th percentile100
Maximum100
Range63
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.1432
Coefficient of variation (CV)0.098933961
Kurtosis4.7068812
Mean92.417203
Median Absolute Deviation (MAD)3
Skewness-2.0489045
Sum818724
Variance83.598105
MonotonicityNot monotonic
2023-04-11T05:27:17.511186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1299
 
7.4%
98 1129
 
6.4%
97 921
 
5.2%
99 691
 
3.9%
96 628
 
3.6%
95 546
 
3.1%
94 415
 
2.3%
93 319
 
1.8%
92 274
 
1.6%
91 270
 
1.5%
Other values (50) 2367
 
13.4%
(Missing) 8810
49.9%
ValueCountFrequency (%)
37 1
 
< 0.1%
39 1
 
< 0.1%
41 1
 
< 0.1%
42 1
 
< 0.1%
44 2
 
< 0.1%
45 4
< 0.1%
46 2
 
< 0.1%
47 5
< 0.1%
48 4
< 0.1%
50 3
< 0.1%
ValueCountFrequency (%)
100 1299
7.4%
99 691
3.9%
98 1129
6.4%
97 921
5.2%
96 628
3.6%
95 546
3.1%
94 415
 
2.3%
93 319
 
1.8%
92 274
 
1.6%
91 270
 
1.5%

salaire_net_median_des_emplois_a_temps_plein
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct149
Distinct (%)2.0%
Missing10111
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean1900.0437
Minimum1280
Maximum3150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:17.668205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1530
Q11700
median1880
Q32060
95-th percentile2350
Maximum3150
Range1870
Interquartile range (IQR)360

Descriptive statistics

Standard deviation249.70447
Coefficient of variation (CV)0.13142038
Kurtosis0.30107544
Mean1900.0437
Median Absolute Deviation (MAD)180
Skewness0.56215244
Sum14360530
Variance62352.32
MonotonicityNot monotonic
2023-04-11T05:27:17.830163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 482
 
2.7%
1800 441
 
2.5%
1700 416
 
2.4%
1900 282
 
1.6%
1600 280
 
1.6%
2100 232
 
1.3%
1950 199
 
1.1%
1750 177
 
1.0%
1500 150
 
0.8%
1730 147
 
0.8%
Other values (139) 4752
26.9%
(Missing) 10111
57.2%
ValueCountFrequency (%)
1280 1
 
< 0.1%
1300 2
 
< 0.1%
1330 3
 
< 0.1%
1350 4
 
< 0.1%
1360 2
 
< 0.1%
1370 1
 
< 0.1%
1380 5
 
< 0.1%
1400 22
0.1%
1410 9
0.1%
1420 4
 
< 0.1%
ValueCountFrequency (%)
3150 2
< 0.1%
3100 1
 
< 0.1%
3000 2
< 0.1%
2980 1
 
< 0.1%
2900 2
< 0.1%
2800 1
 
< 0.1%
2770 3
< 0.1%
2760 2
< 0.1%
2750 2
< 0.1%
2740 1
 
< 0.1%

salaire_brut_annuel_estime
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct219
Distinct (%)2.9%
Missing10111
Missing (%)57.2%
Infinite0
Infinite (%)0.0%
Mean29635.022
Minimum20000
Maximum49100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:17.988896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile23900
Q126500
median29300
Q332100
95-th percentile36700
Maximum49100
Range29100
Interquartile range (IQR)5600

Descriptive statistics

Standard deviation3894.9219
Coefficient of variation (CV)0.13142969
Kurtosis0.30271191
Mean29635.022
Median Absolute Deviation (MAD)2800
Skewness0.56255104
Sum2.239815 × 108
Variance15170417
MonotonicityNot monotonic
2023-04-11T05:27:18.138441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31200 444
 
2.5%
28100 419
 
2.4%
26500 398
 
2.3%
29600 269
 
1.5%
25000 262
 
1.5%
32800 225
 
1.3%
30400 192
 
1.1%
27300 165
 
0.9%
23400 146
 
0.8%
34300 135
 
0.8%
Other values (209) 4903
27.7%
(Missing) 10111
57.2%
ValueCountFrequency (%)
20000 1
 
< 0.1%
20300 2
 
< 0.1%
20700 2
 
< 0.1%
20800 1
 
< 0.1%
21100 4
 
< 0.1%
21200 2
 
< 0.1%
21300 1
 
< 0.1%
21500 3
 
< 0.1%
21600 2
 
< 0.1%
21800 22
0.1%
ValueCountFrequency (%)
49100 2
< 0.1%
48400 1
 
< 0.1%
46800 2
< 0.1%
46500 1
 
< 0.1%
45200 2
< 0.1%
43700 1
 
< 0.1%
43200 3
< 0.1%
43100 2
< 0.1%
42900 2
< 0.1%
42700 1
 
< 0.1%

de_diplomes_boursiers
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)0.4%
Missing198
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean31.373247
Minimum0
Maximum100
Zeros60
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:18.296678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q127
median32
Q337
95-th percentile47
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.4072919
Coefficient of variation (CV)0.29985076
Kurtosis2.8471239
Mean31.373247
Median Absolute Deviation (MAD)5
Skewness0.18934591
Sum548122
Variance88.497141
MonotonicityNot monotonic
2023-04-11T05:27:18.445827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1137
 
6.4%
33 1063
 
6.0%
32 977
 
5.5%
30 940
 
5.3%
38 883
 
5.0%
31 872
 
4.9%
28 848
 
4.8%
34 728
 
4.1%
36 712
 
4.0%
27 621
 
3.5%
Other values (57) 8690
49.2%
ValueCountFrequency (%)
0 60
0.3%
1 2
 
< 0.1%
2 4
 
< 0.1%
3 2
 
< 0.1%
4 8
 
< 0.1%
5 6
 
< 0.1%
6 10
 
0.1%
7 38
 
0.2%
8 88
0.5%
9 96
0.5%
ValueCountFrequency (%)
100 14
0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 2
 
< 0.1%
64 18
0.1%
63 6
 
< 0.1%
62 28
0.2%
61 4
 
< 0.1%
60 18
0.1%
58 2
 
< 0.1%

taux_de_chomage_regional
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct69
Distinct (%)0.4%
Missing451
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean8.8979905
Minimum5.8
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:18.596926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile6.6
Q17.6
median8.8
Q39.6
95-th percentile11.9
Maximum20.1
Range14.3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9050812
Coefficient of variation (CV)0.21410241
Kurtosis6.4411123
Mean8.8979905
Median Absolute Deviation (MAD)1.1
Skewness1.8460171
Sum153205.6
Variance3.6293344
MonotonicityNot monotonic
2023-04-11T05:27:18.738086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1075
 
6.1%
8.8 1029
 
5.8%
7.1 890
 
5.0%
8.3 804
 
4.6%
8.9 789
 
4.5%
7.6 698
 
4.0%
7.8 656
 
3.7%
7.7 642
 
3.6%
8.6 528
 
3.0%
7 464
 
2.6%
Other values (59) 9643
54.6%
ValueCountFrequency (%)
5.8 116
 
0.7%
6 92
 
0.5%
6.2 118
 
0.7%
6.4 218
1.2%
6.5 118
 
0.7%
6.6 306
1.7%
6.7 212
1.2%
6.8 96
 
0.5%
6.9 92
 
0.5%
7 464
2.6%
ValueCountFrequency (%)
20.1 30
 
0.2%
19.7 12
 
0.1%
19.4 26
 
0.1%
19.2 32
 
0.2%
18 16
 
0.1%
17.8 14
 
0.1%
17.4 30
 
0.2%
15.5 28
 
0.2%
14.3 128
0.7%
14 65
0.4%

salaire_net_mensuel_median_regional
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)0.3%
Missing590
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean1856.1596
Minimum1580
Maximum2240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:18.895779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1580
5-th percentile1710
Q11770
median1820
Q31890
95-th percentile2120
Maximum2240
Range660
Interquartile range (IQR)120

Descriptive statistics

Standard deviation128.67351
Coefficient of variation (CV)0.069322438
Kurtosis0.75659699
Mean1856.1596
Median Absolute Deviation (MAD)60
Skewness1.1662304
Sum31701350
Variance16556.872
MonotonicityNot monotonic
2023-04-11T05:27:19.040996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1780 1322
 
7.5%
1830 1247
 
7.1%
1760 966
 
5.5%
1750 943
 
5.3%
1800 895
 
5.1%
1810 796
 
4.5%
1820 736
 
4.2%
1860 670
 
3.8%
1840 642
 
3.6%
1770 533
 
3.0%
Other values (35) 8329
47.1%
(Missing) 590
 
3.3%
ValueCountFrequency (%)
1580 15
 
0.1%
1650 105
0.6%
1660 119
0.7%
1670 79
 
0.4%
1680 188
1.1%
1690 111
0.6%
1700 112
0.6%
1710 235
1.3%
1720 179
1.0%
1725 164
0.9%
ValueCountFrequency (%)
2240 374
2.1%
2170 340
1.9%
2120 374
2.1%
2090 15
 
0.1%
2070 426
2.4%
2060 408
2.3%
2050 425
2.4%
2020 422
2.4%
1990 421
2.4%
1980 448
2.5%

emplois_cadre
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)1.2%
Missing9313
Missing (%)52.7%
Infinite0
Infinite (%)0.0%
Mean63.814265
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:19.201263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q150
median65
Q379
95-th percentile94
Maximum100
Range99
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.492925
Coefficient of variation (CV)0.30546344
Kurtosis-0.64503563
Mean63.814265
Median Absolute Deviation (MAD)15
Skewness-0.22759413
Sum533232
Variance379.97412
MonotonicityNot monotonic
2023-04-11T05:27:19.371599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 178
 
1.0%
63 164
 
0.9%
65 159
 
0.9%
67 157
 
0.9%
71 156
 
0.9%
80 155
 
0.9%
75 152
 
0.9%
52 150
 
0.8%
50 149
 
0.8%
62 148
 
0.8%
Other values (88) 6788
38.4%
(Missing) 9313
52.7%
ValueCountFrequency (%)
1 3
< 0.1%
2 3
< 0.1%
3 1
 
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 1
 
< 0.1%
9 2
< 0.1%
10 3
< 0.1%
11 4
< 0.1%
12 3
< 0.1%
ValueCountFrequency (%)
100 87
0.5%
99 5
 
< 0.1%
98 43
0.2%
97 99
0.6%
96 67
0.4%
95 71
0.4%
94 73
0.4%
93 104
0.6%
92 88
0.5%
91 94
0.5%

emplois_exterieurs_a_la_region_de_luniversite
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)1.1%
Missing9143
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean42.197279
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:19.527491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q128
median43
Q355
95-th percentile72
Maximum100
Range98
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.716086
Coefficient of variation (CV)0.44353774
Kurtosis-0.23637575
Mean42.197279
Median Absolute Deviation (MAD)13
Skewness0.15965443
Sum359774
Variance350.29187
MonotonicityNot monotonic
2023-04-11T05:27:19.684572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 227
 
1.3%
39 210
 
1.2%
50 203
 
1.1%
51 198
 
1.1%
40 192
 
1.1%
54 191
 
1.1%
41 183
 
1.0%
45 182
 
1.0%
46 173
 
1.0%
42 172
 
1.0%
Other values (86) 6595
37.3%
(Missing) 9143
51.7%
ValueCountFrequency (%)
2 12
 
0.1%
3 15
 
0.1%
4 22
 
0.1%
5 20
 
0.1%
6 37
0.2%
7 42
0.2%
8 62
0.4%
9 45
0.3%
10 46
0.3%
11 69
0.4%
ValueCountFrequency (%)
100 56
0.3%
98 4
 
< 0.1%
97 8
 
< 0.1%
96 6
 
< 0.1%
95 2
 
< 0.1%
93 6
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 2
 
< 0.1%
88 12
 
0.1%

femmes
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)1.1%
Missing8387
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean59.326546
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:19.859885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile18
Q148
median62
Q374
95-th percentile87
Maximum100
Range98
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.873605
Coefficient of variation (CV)0.33498671
Kurtosis-0.0099150322
Mean59.326546
Median Absolute Deviation (MAD)13
Skewness-0.67077201
Sum550669
Variance394.96016
MonotonicityNot monotonic
2023-04-11T05:27:20.013634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 248
 
1.4%
60 245
 
1.4%
61 209
 
1.2%
56 209
 
1.2%
76 208
 
1.2%
73 201
 
1.1%
66 200
 
1.1%
65 198
 
1.1%
63 196
 
1.1%
59 195
 
1.1%
Other values (88) 7173
40.6%
(Missing) 8387
47.5%
ValueCountFrequency (%)
2 10
 
0.1%
3 16
0.1%
4 10
 
0.1%
5 11
 
0.1%
6 34
0.2%
7 15
0.1%
8 25
0.1%
9 36
0.2%
10 31
0.2%
11 31
0.2%
ValueCountFrequency (%)
100 4
 
< 0.1%
98 2
 
< 0.1%
97 14
 
0.1%
96 6
 
< 0.1%
95 10
 
0.1%
94 14
 
0.1%
93 36
0.2%
92 53
0.3%
91 35
0.2%
90 66
0.4%

salaire_net_mensuel_regional_1er_quartile
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)0.2%
Missing2585
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean1472.0399
Minimum1310
Maximum1780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:20.162077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1310
5-th percentile1380
Q11410
median1450
Q31490
95-th percentile1640
Maximum1780
Range470
Interquartile range (IQR)80

Descriptive statistics

Standard deviation87.278492
Coefficient of variation (CV)0.059290846
Kurtosis0.73172439
Mean1472.0399
Median Absolute Deviation (MAD)40
Skewness1.1266049
Sum22204250
Variance7617.5351
MonotonicityNot monotonic
2023-04-11T05:27:20.290518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1470 1394
 
7.9%
1430 1376
 
7.8%
1400 1056
 
6.0%
1380 1054
 
6.0%
1450 938
 
5.3%
1420 902
 
5.1%
1460 862
 
4.9%
1480 718
 
4.1%
1390 650
 
3.7%
1410 564
 
3.2%
Other values (21) 5570
31.5%
(Missing) 2585
14.6%
ValueCountFrequency (%)
1310 86
 
0.5%
1340 26
 
0.1%
1350 86
 
0.5%
1360 72
 
0.4%
1370 286
 
1.6%
1380 1054
6.0%
1390 650
3.7%
1400 1056
6.0%
1405 114
 
0.6%
1410 564
3.2%
ValueCountFrequency (%)
1780 174
1.0%
1650 340
1.9%
1640 374
2.1%
1630 426
2.4%
1620 374
2.1%
1610 410
2.3%
1600 408
2.3%
1580 422
2.4%
1550 412
2.3%
1500 124
 
0.7%

salaire_net_mensuel_regional_3eme_quartile
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)0.3%
Missing2583
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean2242.6236
Minimum1980
Maximum2790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size138.2 KiB
2023-04-11T05:27:20.436083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile2030
Q12110
median2180
Q32310
95-th percentile2620
Maximum2790
Range810
Interquartile range (IQR)200

Descriptive statistics

Standard deviation200.13152
Coefficient of variation (CV)0.089239906
Kurtosis0.27719826
Mean2242.6236
Median Absolute Deviation (MAD)90
Skewness1.1697567
Sum33832220
Variance40052.626
MonotonicityNot monotonic
2023-04-11T05:27:20.581351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2180 988
 
5.6%
2120 902
 
5.1%
2060 788
 
4.5%
2130 764
 
4.3%
2220 592
 
3.4%
2100 572
 
3.2%
2140 554
 
3.1%
2080 504
 
2.9%
2210 456
 
2.6%
2150 450
 
2.5%
Other values (33) 8516
48.2%
(Missing) 2583
 
14.6%
ValueCountFrequency (%)
1980 88
 
0.5%
2010 246
 
1.4%
2020 166
 
0.9%
2025 164
 
0.9%
2030 206
 
1.2%
2040 174
 
1.0%
2050 146
 
0.8%
2060 788
4.5%
2070 414
2.3%
2080 504
2.9%
ValueCountFrequency (%)
2790 374
2.1%
2700 340
1.9%
2620 374
2.1%
2580 426
2.4%
2570 408
2.3%
2560 410
2.3%
2530 422
2.4%
2480 36
 
0.2%
2420 412
2.3%
2380 36
 
0.2%

cle_etab
Categorical

Distinct185
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
Dijon - Bourgogne_30
 
181
Strasbourg_30
 
181
Toutes universités et établissements assimilés_30
 
181
Nantes_30
 
180
Aix-Marseille_30
 
180
Other values (180)
16766 

Length

Max length51
Median length34
Mean length20.763031
Min length8

Characters and Unicode

Total characters366862
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNice - Sophia-Antipolis_30
2nd rowNice - Sophia-Antipolis_30
3rd rowAix-Marseille_30
4th rowAix-Marseille_30
5th rowCaen Normandie_30

Common Values

ValueCountFrequency (%)
Dijon - Bourgogne_30 181
 
1.0%
Strasbourg_30 181
 
1.0%
Toutes universités et établissements assimilés_30 181
 
1.0%
Nantes_30 180
 
1.0%
Aix-Marseille_30 180
 
1.0%
Université Sorbonne Paris Nord_30 179
 
1.0%
Besançon - Franche-Comté_30 179
 
1.0%
Poitiers_30 177
 
1.0%
Lorraine_30 177
 
1.0%
Amiens - Picardie Jules-Verne_30 175
 
1.0%
Other values (175) 15879
89.9%

Length

2023-04-11T05:27:20.742995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6405
 
13.5%
paris 2095
 
4.4%
1 862
 
1.8%
3 786
 
1.7%
jean 739
 
1.6%
université 734
 
1.6%
lyon 691
 
1.5%
2 628
 
1.3%
de 612
 
1.3%
toulouse 601
 
1.3%
Other values (243) 33201
70.1%

Most occurring characters

ValueCountFrequency (%)
e 33949
 
9.3%
29685
 
8.1%
n 24929
 
6.8%
a 21123
 
5.8%
i 20625
 
5.6%
s 19248
 
5.2%
r 19185
 
5.2%
_ 17669
 
4.8%
o 17441
 
4.8%
l 13790
 
3.8%
Other values (53) 149218
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 227991
62.1%
Uppercase Letter 39997
 
10.9%
Decimal Number 38831
 
10.6%
Space Separator 29685
 
8.1%
Connector Punctuation 17669
 
4.8%
Dash Punctuation 11925
 
3.3%
Other Punctuation 764
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33949
14.9%
n 24929
10.9%
a 21123
9.3%
i 20625
9.0%
s 19248
8.4%
r 19185
8.4%
o 17441
7.6%
l 13790
 
6.0%
t 13315
 
5.8%
u 9081
 
4.0%
Other values (19) 35305
15.5%
Uppercase Letter
ValueCountFrequency (%)
P 6254
15.6%
A 3434
 
8.6%
S 3413
 
8.5%
L 3237
 
8.1%
C 3120
 
7.8%
M 2617
 
6.5%
B 2541
 
6.4%
N 1864
 
4.7%
R 1740
 
4.4%
V 1702
 
4.3%
Other values (13) 10075
25.2%
Decimal Number
ValueCountFrequency (%)
3 10633
27.4%
0 9847
25.4%
1 9237
23.8%
8 8081
20.8%
2 849
 
2.2%
7 132
 
0.3%
4 52
 
0.1%
Space Separator
ValueCountFrequency (%)
29685
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17669
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11925
100.0%
Other Punctuation
ValueCountFrequency (%)
' 764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 267988
73.0%
Common 98874
 
27.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33949
12.7%
n 24929
 
9.3%
a 21123
 
7.9%
i 20625
 
7.7%
s 19248
 
7.2%
r 19185
 
7.2%
o 17441
 
6.5%
l 13790
 
5.1%
t 13315
 
5.0%
u 9081
 
3.4%
Other values (42) 75302
28.1%
Common
ValueCountFrequency (%)
29685
30.0%
_ 17669
17.9%
- 11925
12.1%
3 10633
 
10.8%
0 9847
 
10.0%
1 9237
 
9.3%
8 8081
 
8.2%
2 849
 
0.9%
' 764
 
0.8%
7 132
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361304
98.5%
None 5558
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33949
 
9.4%
29685
 
8.2%
n 24929
 
6.9%
a 21123
 
5.8%
i 20625
 
5.7%
s 19248
 
5.3%
r 19185
 
5.3%
_ 17669
 
4.9%
o 17441
 
4.8%
l 13790
 
3.8%
Other values (47) 143660
39.8%
None
ValueCountFrequency (%)
é 4121
74.1%
ç 491
 
8.8%
è 310
 
5.6%
ô 277
 
5.0%
É 219
 
3.9%
î 140
 
2.5%

cle_disc
Categorical

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size138.2 KiB
disc07_30
 
684
disc01_30
 
670
disc12_30
 
669
disc04_30
 
600
disc11_30
 
596
Other values (33)
14450 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters159021
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdisc04_30
2nd rowdisc08_30
3rd rowdisc08_30
4th rowdisc10_30
5th rowdisc01_30

Common Values

ValueCountFrequency (%)
disc07_30 684
 
3.9%
disc01_30 670
 
3.8%
disc12_30 669
 
3.8%
disc04_30 600
 
3.4%
disc11_30 596
 
3.4%
disc06_30 585
 
3.3%
disc16_30 575
 
3.3%
disc02_30 574
 
3.2%
disc18_30 551
 
3.1%
disc14_30 546
 
3.1%
Other values (28) 11619
65.8%

Length

2023-04-11T05:27:20.864567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
disc07_30 684
 
3.9%
disc01_30 670
 
3.8%
disc12_30 669
 
3.8%
disc04_30 600
 
3.4%
disc11_30 596
 
3.4%
disc06_30 585
 
3.3%
disc16_30 575
 
3.3%
disc02_30 574
 
3.2%
disc18_30 551
 
3.1%
disc14_30 546
 
3.1%
Other values (28) 11619
65.8%

Most occurring characters

ValueCountFrequency (%)
0 19486
12.3%
1 18922
11.9%
d 17669
11.1%
i 17669
11.1%
s 17669
11.1%
c 17669
11.1%
_ 17669
11.1%
3 11771
7.4%
8 9781
6.2%
2 2296
 
1.4%
Other values (5) 8420
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70676
44.4%
Lowercase Letter 70676
44.4%
Connector Punctuation 17669
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19486
27.6%
1 18922
26.8%
3 11771
16.7%
8 9781
13.8%
2 2296
 
3.2%
6 2091
 
3.0%
7 2089
 
3.0%
4 2056
 
2.9%
5 1580
 
2.2%
9 604
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
d 17669
25.0%
i 17669
25.0%
s 17669
25.0%
c 17669
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88345
55.6%
Latin 70676
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19486
22.1%
1 18922
21.4%
_ 17669
20.0%
3 11771
13.3%
8 9781
11.1%
2 2296
 
2.6%
6 2091
 
2.4%
7 2089
 
2.4%
4 2056
 
2.3%
5 1580
 
1.8%
Latin
ValueCountFrequency (%)
d 17669
25.0%
i 17669
25.0%
s 17669
25.0%
c 17669
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19486
12.3%
1 18922
11.9%
d 17669
11.1%
i 17669
11.1%
s 17669
11.1%
c 17669
11.1%
_ 17669
11.1%
3 11771
7.4%
8 9781
6.2%
2 2296
 
1.4%
Other values (5) 8420
5.3%

id_paysage
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)0.6%
Missing325
Missing (%)1.8%
Memory size138.2 KiB
Lr94O
 
325
4k25D
 
325
cqyN7
 
323
xJdyB
 
323
7hB8r
 
323
Other values (95)
15725 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters86720
Distinct characters61
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7CYWd
2nd row7CYWd
3rd rowxJdyB
4th rowxJdyB
5th rowp25Q3

Common Values

ValueCountFrequency (%)
Lr94O 325
 
1.8%
4k25D 325
 
1.8%
cqyN7 323
 
1.8%
xJdyB 323
 
1.8%
7hB8r 323
 
1.8%
7Mpgt 321
 
1.8%
hlX1r 319
 
1.8%
t6Cq5 319
 
1.8%
8j5s2 315
 
1.8%
57OsX 311
 
1.8%
Other values (90) 14140
80.0%
(Missing) 325
 
1.8%

Length

2023-04-11T05:27:20.976855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lr94o 325
 
1.9%
4k25d 325
 
1.9%
cqyn7 323
 
1.9%
xjdyb 323
 
1.9%
7hb8r 323
 
1.9%
7mpgt 321
 
1.9%
hlx1r 319
 
1.8%
t6cq5 319
 
1.8%
8j5s2 315
 
1.8%
57osx 311
 
1.8%
Other values (90) 14140
81.5%

Most occurring characters

ValueCountFrequency (%)
7 4408
 
5.1%
5 3740
 
4.3%
2 3622
 
4.2%
4 2985
 
3.4%
C 2772
 
3.2%
t 2567
 
3.0%
6 2539
 
2.9%
B 2411
 
2.8%
9 2253
 
2.6%
y 2191
 
2.5%
Other values (51) 57232
66.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30486
35.2%
Uppercase Letter 29917
34.5%
Decimal Number 26317
30.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2772
 
9.3%
B 2411
 
8.1%
H 1824
 
6.1%
U 1612
 
5.4%
W 1438
 
4.8%
L 1313
 
4.4%
Y 1313
 
4.4%
O 1307
 
4.4%
X 1223
 
4.1%
J 1210
 
4.0%
Other values (16) 13494
45.1%
Lowercase Letter
ValueCountFrequency (%)
t 2567
 
8.4%
y 2191
 
7.2%
v 1950
 
6.4%
r 1773
 
5.8%
h 1743
 
5.7%
k 1704
 
5.6%
q 1444
 
4.7%
z 1440
 
4.7%
d 1396
 
4.6%
b 1393
 
4.6%
Other values (15) 12885
42.3%
Decimal Number
ValueCountFrequency (%)
7 4408
16.7%
5 3740
14.2%
2 3622
13.8%
4 2985
11.3%
6 2539
9.6%
9 2253
8.6%
1 2145
8.2%
8 1787
6.8%
0 1463
 
5.6%
3 1375
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 60403
69.7%
Common 26317
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2772
 
4.6%
t 2567
 
4.2%
B 2411
 
4.0%
y 2191
 
3.6%
v 1950
 
3.2%
H 1824
 
3.0%
r 1773
 
2.9%
h 1743
 
2.9%
k 1704
 
2.8%
U 1612
 
2.7%
Other values (41) 39856
66.0%
Common
ValueCountFrequency (%)
7 4408
16.7%
5 3740
14.2%
2 3622
13.8%
4 2985
11.3%
6 2539
9.6%
9 2253
8.6%
1 2145
8.2%
8 1787
6.8%
0 1463
 
5.6%
3 1375
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 4408
 
5.1%
5 3740
 
4.3%
2 3622
 
4.2%
4 2985
 
3.4%
C 2772
 
3.2%
t 2567
 
3.0%
6 2539
 
2.9%
B 2411
 
2.8%
9 2253
 
2.6%
y 2191
 
2.5%
Other values (51) 57232
66.0%

Interactions

2023-04-11T05:27:09.175712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:28.649158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.815310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.775876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.054001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.313725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.498574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:42.943001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:45.113404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:47.921377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.293359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.613947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.828016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.184604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.490080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.708795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.716794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:06.939240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.316042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:28.770615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.936251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.896247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.169314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.423905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.692575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.050903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:45.228406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.047052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.428081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.726546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.964370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.305882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.622661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.843011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.847516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.068782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.461390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:28.891690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:31.065283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.023049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.294641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.550308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.844217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.178269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:45.355544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.179018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.567995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.856296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:55.098139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.449834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.750340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.977023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.973560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.197574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.607394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.026904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:31.186185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.136348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.409385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.683740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:41.046850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.300004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:46.053068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.315751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.698934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.971397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:55.219211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.579545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.873476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:02.103117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:05.105290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.315942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.732517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.137344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:31.305707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.252900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.527741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.796186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:41.183911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.415820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:46.190073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.438028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.820241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:53.095479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:55.337878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.706692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.988760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:02.905922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:05.219303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.446847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.854491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.249049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:31.423780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.374913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.643814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.903964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:41.316594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.522722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:46.302128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.572069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.953580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:53.215931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:55.458776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.838132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:00.100793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:03.031792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:05.329589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.561234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.991225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.364749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:32.212621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.504675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.760736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:39.019525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:41.444751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.636083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:46.420329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.696040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:51.078146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:53.338606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:55.580004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.983678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:00.218098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:03.156478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:05.466839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.685711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:10.133541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.473562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:32.330537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.618994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:36.885662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:39.143023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:41.561176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:43.743326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:46.531546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:48.838123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:51.216445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:53.463017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:55.698401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:58.102488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:00.328972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:03.273355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:05.593297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:07.800225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:10.279112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.593204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:32.456359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.746709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:37.009259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:39.261833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:27:07.928750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:10.420619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.717459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:32.589576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:34.889494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:37.135550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:39.393222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:41.825005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:55.963525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:53.858638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:56.096874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:58.500656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:00.728142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:03.660581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:05.972095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:08.186444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:10.706193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:29.945665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:42.079936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:44.275458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:49.378322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:27:06.092940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:27:10.843984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.068076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:32.977947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:35.279943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:37.537505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:39.766219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:42.207231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:44.398723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:47.155414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:49.514405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:51.858162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.096248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:56.348398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:58.737484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:00.982563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:03.913021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:06.218573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:08.421215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:10.992486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.185267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.117445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:37.682133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:39.893553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:44.518189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:51.985903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.219509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:56.477160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:58.869251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.106576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.039921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:06.339771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:08.558963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:11.121231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.307182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.240542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:35.546974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:37.815533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.013486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:42.449116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:44.630298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:47.402098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:49.768621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.110800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.336043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:56.617587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:58.989425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:27:08.678509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:11.254258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.432132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.363922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:35.671605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:37.947363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.126861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:42.570068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:44.743204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:47.530402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:49.892058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.233488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.457898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:56.758089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.111999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.339245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.316857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:06.572828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:08.801481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:11.383055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.553989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.488863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:35.790956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.064465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.236494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:42.687648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:44.857136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-04-11T05:26:50.019986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.354153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.576262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:56.906385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.230861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.452110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.447390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:06.685886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:08.918544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:11.514890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:30.676003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:33.625164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:35.921246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:38.181616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:40.350419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:42.806544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:44.973686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:47.787352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:50.149793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:52.476847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:54.696182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:57.030250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:26:59.353481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:01.574127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:04.583279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:06.807398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T05:27:09.039448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-11T05:27:21.967191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
anneenombre_de_reponsestaux_de_reponsepoids_de_la_disciplinetaux_dinsertionemplois_cadre_ou_professions_intermediairesemplois_stablesemplois_a_temps_pleinsalaire_net_median_des_emplois_a_temps_pleinsalaire_brut_annuel_estimede_diplomes_boursierstaux_de_chomage_regionalsalaire_net_mensuel_median_regionalemplois_cadreemplois_exterieurs_a_la_region_de_luniversitefemmessalaire_net_mensuel_regional_1er_quartilesalaire_net_mensuel_regional_3eme_quartilediplomenumero_de_l_etablissementetablissementcode_de_l_academieacademiecode_du_domainedomainecode_de_la_disciplinedisciplinesituationcle_discid_paysage
annee1.0000.0140.012-0.0010.1100.1580.0500.0880.2070.2060.089-0.6140.4310.091-0.025-0.0290.0340.3900.1070.1730.1500.0000.0000.0510.0510.0970.0970.3200.1380.179
nombre_de_reponses0.0141.0000.0530.7540.070-0.0010.0460.0130.0500.050-0.143-0.0390.0160.036-0.020-0.0240.0100.0400.0820.3120.3121.0001.0000.0580.0580.0850.0850.0000.0801.000
taux_de_reponse0.0120.0531.000-0.076-0.0810.040-0.140-0.100-0.192-0.1920.1370.090-0.274-0.0460.239-0.059-0.261-0.2550.0520.2810.2810.2020.2020.0920.0920.0820.0820.0080.0770.281
poids_de_la_discipline-0.0010.754-0.0761.0000.185-0.0160.2230.052-0.012-0.012-0.0290.019-0.0090.051-0.1250.098-0.035-0.0140.8880.1980.1990.1010.1010.4670.4670.4200.4200.0120.4200.199
taux_dinsertion0.1100.070-0.0810.1851.0000.4090.6180.3250.3620.362-0.153-0.1250.0550.460-0.214-0.1150.0600.0590.2790.1450.1460.1100.1100.2140.2140.2060.2060.2410.2420.155
emplois_cadre_ou_professions_intermediaires0.158-0.0010.040-0.0160.4091.0000.4940.4030.5390.539-0.233-0.1460.1490.804-0.142-0.3360.1270.1500.1540.1650.1660.0970.0970.2640.2640.2080.2080.1290.2140.165
emplois_stables0.0500.046-0.1400.2230.6180.4941.0000.5780.5990.599-0.197-0.0200.0740.493-0.231-0.3760.0600.0980.3570.1540.1530.0830.0830.3520.3520.3050.3050.3470.3770.155
emplois_a_temps_plein0.0880.013-0.1000.0520.3250.4030.5781.0000.6500.650-0.151-0.0610.0930.237-0.034-0.5640.0710.0790.2270.1530.1540.0590.0590.3910.3910.3520.3520.0750.3660.155
salaire_net_median_des_emplois_a_temps_plein0.2070.050-0.192-0.0120.3620.5390.5990.6501.0001.000-0.307-0.1880.3500.499-0.175-0.5600.2670.3580.3540.2430.2430.1720.1720.3410.3410.2620.2620.2000.2850.250
salaire_brut_annuel_estime0.2060.050-0.192-0.0120.3620.5390.5990.6501.0001.000-0.307-0.1870.3500.499-0.175-0.5610.2670.3580.3520.2430.2430.1720.1720.3400.3400.2610.2610.2000.2840.250
de_diplomes_boursiers0.089-0.1430.137-0.029-0.153-0.233-0.197-0.151-0.307-0.3071.0000.080-0.446-0.3160.4520.023-0.537-0.4430.2370.5890.5890.4190.4190.1220.1220.1010.1010.0240.0970.594
taux_de_chomage_regional-0.614-0.0390.0900.019-0.125-0.146-0.020-0.061-0.188-0.1870.0801.000-0.387-0.1410.097-0.031-0.221-0.3720.0110.5970.5950.5670.5670.0000.0000.0000.0000.0750.0000.598
salaire_net_mensuel_median_regional0.4310.016-0.274-0.0090.0550.1490.0740.0930.3500.350-0.446-0.3871.0000.192-0.5650.0220.7990.9760.0240.5100.4740.4170.4170.0100.0100.0330.0330.1660.0550.496
emplois_cadre0.0910.036-0.0460.0510.4600.8040.4930.2370.4990.499-0.316-0.1410.1921.000-0.278-0.1920.1680.2090.3800.2600.2340.1650.1650.2870.2870.2910.2910.1080.2950.259
emplois_exterieurs_a_la_region_de_luniversite-0.025-0.0200.239-0.125-0.214-0.142-0.231-0.034-0.175-0.1750.4520.097-0.565-0.2781.000-0.115-0.549-0.5560.3040.4010.4010.3630.3630.1990.1990.1700.1700.0000.1640.400
femmes-0.029-0.024-0.0590.098-0.115-0.336-0.376-0.564-0.560-0.5610.023-0.0310.022-0.192-0.1151.0000.0410.0380.3320.1970.1970.0960.0960.4450.4450.4690.4690.0000.4670.200
salaire_net_mensuel_regional_1er_quartile0.0340.010-0.261-0.0350.0600.1270.0600.0710.2670.267-0.537-0.2210.7990.168-0.5490.0411.0000.7340.0000.5750.5710.5450.5450.0000.0000.0000.0000.0000.0000.572
salaire_net_mensuel_regional_3eme_quartile0.3900.040-0.255-0.0140.0590.1500.0980.0790.3580.358-0.443-0.3720.9760.209-0.5560.0380.7341.0000.0000.4920.4690.4080.4080.0000.0000.0000.0000.0000.0000.484
diplome0.1070.0820.0520.8880.2790.1540.3570.2270.3540.3520.2370.0110.0240.3800.3040.3320.0000.0001.0000.0840.0850.0260.0261.0001.0000.9990.9990.0040.9990.084
numero_de_l_etablissement0.1730.3120.2810.1980.1450.1650.1540.1530.2430.2430.5890.5970.5100.2600.4010.1970.5750.4920.0841.0001.0000.9980.9980.1420.1420.0910.0910.0190.0430.984
etablissement0.1500.3120.2810.1990.1460.1660.1530.1540.2430.2430.5890.5950.4740.2340.4010.1970.5710.4690.0851.0001.0000.9980.9980.1430.1430.0920.0920.0240.0460.994
code_de_l_academie0.0001.0000.2020.1010.1100.0970.0830.0590.1720.1720.4190.5670.4170.1650.3630.0960.5450.4080.0260.9980.9981.0001.0000.0390.0390.0370.0370.0000.0000.998
academie0.0001.0000.2020.1010.1100.0970.0830.0590.1720.1720.4190.5670.4170.1650.3630.0960.5450.4080.0260.9980.9981.0001.0000.0390.0390.0370.0370.0000.0000.998
code_du_domaine0.0510.0580.0920.4670.2140.2640.3520.3910.3410.3400.1220.0000.0100.2870.1990.4450.0000.0001.0000.1420.1430.0390.0391.0001.0001.0001.0000.0000.9990.144
domaine0.0510.0580.0920.4670.2140.2640.3520.3910.3410.3400.1220.0000.0100.2870.1990.4450.0000.0001.0000.1420.1430.0390.0391.0001.0001.0001.0000.0000.9990.144
code_de_la_discipline0.0970.0850.0820.4200.2060.2080.3050.3520.2620.2610.1010.0000.0330.2910.1700.4690.0000.0000.9990.0910.0920.0370.0371.0001.0001.0001.0000.0610.9990.093
discipline0.0970.0850.0820.4200.2060.2080.3050.3520.2620.2610.1010.0000.0330.2910.1700.4690.0000.0000.9990.0910.0920.0370.0371.0001.0001.0001.0000.0610.9990.093
situation0.3200.0000.0080.0120.2410.1290.3470.0750.2000.2000.0240.0750.1660.1080.0000.0000.0000.0000.0040.0190.0240.0000.0000.0000.0000.0610.0611.0000.9990.024
cle_disc0.1380.0800.0770.4200.2420.2140.3770.3660.2850.2840.0970.0000.0550.2950.1640.4670.0000.0000.9990.0430.0460.0000.0000.9990.9990.9990.9990.9991.0000.044
id_paysage0.1791.0000.2810.1990.1550.1650.1550.1550.2500.2500.5940.5980.4960.2590.4000.2000.5720.4840.0840.9840.9940.9980.9980.1440.1440.0930.0930.0240.0441.000

Missing values

2023-04-11T05:27:11.784618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-11T05:27:12.344684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-11T05:27:12.884787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

anneediplomenumero_de_l_etablissementetablissementcode_de_l_academieacademiecode_du_domainedomainecode_de_la_disciplinedisciplinesituationnombre_de_reponsestaux_de_reponsepoids_de_la_disciplinetaux_dinsertionemplois_cadre_ou_professions_intermediairesemplois_stablesemplois_a_temps_pleinsalaire_net_median_des_emplois_a_temps_pleinsalaire_brut_annuel_estimede_diplomes_boursierstaux_de_chomage_regionalsalaire_net_mensuel_median_regionalemplois_cadreemplois_exterieurs_a_la_region_de_luniversitefemmessalaire_net_mensuel_regional_1er_quartilesalaire_net_mensuel_regional_3eme_quartilecle_etabcle_discid_paysage
02010MASTER LMD0060931ENice - Sophia-AntipolisA23NiceDEGDroit, économie et gestiondisc04Gestion30 mois après le diplôme167.088.029.088.082.083.096.02000.031200.029.011.71750.0NaNNaNNaNNaNNaNNice - Sophia-Antipolis_30disc04_307CYWd
12010MASTER LMD0060931ENice - Sophia-AntipolisA23NiceSHSSciences humaines et socialesdisc08Histoire-géographie30 mois après le diplôme9.075.02.0NaNNaNNaNNaNNaNNaN29.011.71750.0NaNNaNNaNNaNNaNNice - Sophia-Antipolis_30disc08_307CYWd
22010MASTER LMD0134009MAix-MarseilleA02Aix-MarseilleSHSSciences humaines et socialesdisc08Histoire-géographie30 mois après le diplôme40.077.03.080.075.065.090.0NaNNaN29.011.71750.0NaNNaNNaNNaNNaNAix-Marseille_30disc08_30xJdyB
32010MASTER LMD0134009MAix-MarseilleA02Aix-MarseilleSHSSciences humaines et socialesdisc10Information communication30 mois après le diplôme66.076.04.089.078.064.090.01750.027300.029.011.71750.0NaNNaNNaNNaNNaNAix-Marseille_30disc10_30xJdyB
42010MASTER LMD0141408ECaen NormandieA70NormandieDEGDroit, économie et gestiondisc01Ensemble formations juridiques, économiques et de gestion30 mois après le diplôme255.076.055.093.092.085.098.02110.032900.026.09.71680.0NaNNaNNaNNaNNaNCaen Normandie_30disc01_30p25Q3
52010MASTER LMD0141408ECaen NormandieA70NormandieSTSSciences, technologies et santédisc12Ensemble sciences, technologies et santé30 mois après le diplôme106.084.021.091.094.080.095.01920.029900.026.09.71680.0NaNNaNNaNNaNNaNCaen Normandie_30disc12_30p25Q3
62010MASTER LMD0171463YLa RochelleA13PoitiersSTSSciences, technologies et santédisc12Ensemble sciences, technologies et santé30 mois après le diplôme98.069.049.090.098.081.098.01950.030400.036.09.81670.0NaNNaNNaNNaNNaNLa Rochelle_30disc12_30atbEK
72010MASTER LMD0171463YLa RochelleA13PoitiersSTSSciences, technologies et santédisc13Sciences de la vie et de la terre30 mois après le diplôme45.082.022.082.097.065.095.01750.027300.036.09.81670.0NaNNaNNaNNaNNaNLa Rochelle_30disc13_30atbEK
82010MASTER LMD0251215KBesançon - Franche-ComtéA03BesançonDEGDroit, économie et gestiondisc01Ensemble formations juridiques, économiques et de gestion30 mois après le diplôme80.076.022.086.076.084.0100.01820.028300.038.09.91730.0NaNNaNNaNNaNNaNBesançon - Franche-Comté_30disc01_307Mpgt
92010MASTER LMD0251215KBesançon - Franche-ComtéA03BesançonDEGDroit, économie et gestiondisc05Autres formations juridiques, économiques et de gestion30 mois après le diplôme15.075.04.0NaNNaNNaNNaNNaNNaN38.09.91730.0NaNNaNNaNNaNNaNBesançon - Franche-Comté_30disc05_307Mpgt
anneediplomenumero_de_l_etablissementetablissementcode_de_l_academieacademiecode_du_domainedomainecode_de_la_disciplinedisciplinesituationnombre_de_reponsestaux_de_reponsepoids_de_la_disciplinetaux_dinsertionemplois_cadre_ou_professions_intermediairesemplois_stablesemplois_a_temps_pleinsalaire_net_median_des_emplois_a_temps_pleinsalaire_brut_annuel_estimede_diplomes_boursierstaux_de_chomage_regionalsalaire_net_mensuel_median_regionalemplois_cadreemplois_exterieurs_a_la_region_de_luniversitefemmessalaire_net_mensuel_regional_1er_quartilesalaire_net_mensuel_regional_3eme_quartilecle_etabcle_discid_paysage
176592016MASTER LMD0134009MAix-MarseilleA02Aix-MarseilleSTSSciences, technologies et santédisc12Ensemble sciences, technologies et santé18 mois après le diplôme372.068.027.088.089.068.094.01870.029100.034.010.21800.065.045.044.01410.02140.0Aix-Marseille_18disc12_18xJdyB
176602016MASTER LMD0134009MAix-MarseilleA02Aix-MarseilleSTSSciences, technologies et santédisc12Ensemble sciences, technologies et santé30 mois après le diplôme372.068.027.090.092.080.097.02000.031200.034.010.21800.071.045.044.01410.02140.0Aix-Marseille_30disc12_30xJdyB
176612016MASTER LMD0134009MAix-MarseilleA02Aix-MarseilleSTSSciences, technologies et santédisc13Sciences de la vie et de la terre30 mois après le diplôme100.070.07.086.091.068.094.01820.028400.034.010.21800.068.053.055.01410.02140.0Aix-Marseille_30disc13_30xJdyB
176622016MASTER LMD0134009MAix-MarseilleA02Aix-MarseilleSTSSciences, technologies et santédisc16Informatique18 mois après le diplôme40.066.03.088.0100.092.0100.01980.030900.034.010.21800.097.030.018.01410.02140.0Aix-Marseille_18disc16_18xJdyB
176632016MASTER ENS0141408ECaen NormandieA70NormandieMEEFMasters enseignementdisc18Masters enseignement30 mois après le diplôme123.067.0100.097.087.082.088.01790.027900.026.08.61810.082.017.073.01460.02100.0Caen Normandie_30disc18_30p25Q3
176642016MASTER LMD0141408ECaen NormandieA70NormandieDEGDroit, économie et gestiondisc02Droit30 mois après le diplôme44.076.08.091.095.075.0100.01950.030400.029.08.61810.063.051.076.01460.02100.0Caen Normandie_30disc02_30p25Q3
176652016MASTER LMD0141408ECaen NormandieA70NormandieDEGDroit, économie et gestiondisc03Économie18 mois après le diplôme31.070.06.0NaNNaNNaNNaNNaNNaN29.08.61810.0NaN42.041.01460.02100.0Caen Normandie_18disc03_18p25Q3
176662016MASTER LMD0141408ECaen NormandieA70NormandieDEGDroit, économie et gestiondisc05Autres formations juridiques, économiques et de gestion18 mois après le diplôme43.065.09.096.095.071.097.01950.030400.029.08.61810.068.063.059.01460.02100.0Caen Normandie_18disc05_18p25Q3
176672016MASTER LMD0141408ECaen NormandieA70NormandieLLALettres, langues, artsdisc06Lettres, langues, arts18 mois après le diplôme33.077.06.079.0NaNNaNNaNNaNNaN29.08.61810.0NaN74.069.01460.02100.0Caen Normandie_18disc06_18p25Q3
176682016MASTER LMD0141408ECaen NormandieA70NormandieSHSSciences humaines et socialesdisc07Ensemble sciences humaines et sociales30 mois après le diplôme64.079.011.083.093.061.077.01800.028100.029.08.61810.075.049.073.01460.02100.0Caen Normandie_30disc07_30p25Q3